Machine learning algorithm for autonomous control of walking robot

This work presents our development of autonomous walking robot control using machine learning algorithm. We have investigated sensor driven walking robot movement to develop supervised learning based control algorithm using neural network methods. We used robots hardware data such as pressure sensor data for accurate neural network (NN) classification. The analyzed result shows that ∼25–30 numbers of hidden neurons will perform the best result in terms of mean square error (mse), error-gradient, learning time and regression for small scale data analysis. The analyzed results are useful for next generation FPGA based artificial intelligence (AI) chip development for robot movement control.